Instructions to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with Ollama:
```
ollama run hf.co/EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M
```
Unsloth Desktop

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M
```

Lemonade

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.5-35B-A3B-BigBang-MTP-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Ornith-1.5-35B-A3B-BigBang-MTP — GGUF

GGUF quants of EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP: a TIES merge of Ornith-1.5-35B-A3B + BigBang-v1 over Qwen3.6-35B-A3B, with Ornith's untrained placeholder MTP head replaced by Qwen3.6's trained one — so speculative decoding actually works (~75% draft acceptance vs ~13% for the stock random head; see the main repo card for the measurements).

Requires a recent llama.cpp (b10507+ tested; the Qwen3.5-MoE hybrid arch is new).

Where is the MTP?

Two options, pick one:

Separate draft (recommended): the main GGUFs deliberately contain no MTP tensors. Download mtpdraft-Q8_0.gguf (2 GB) once and pass it with -md — it works with every quant here, and with stock Ornith-1.5 GGUFs too.
Embedded: Ornith-1.5-35B-A3B-BigBang-MTP-Q4_K_M-embedded.gguf carries the trained head inside the main file (stock-style blk.40 layout), for tools that expect it embedded.

If you inspect a non-embedded main GGUF and see no MTP tensors, that is by design, not a missing feature.

Files

(BF16 reference weights live in the main safetensors repo.)

file	size	note
`Ornith-1.5-35B-A3B-BigBang-MTP-Q4_K_M.gguf`	21.2 GB	fits a single 24 GB GPU
`Ornith-1.5-35B-A3B-BigBang-MTP-Q5_K_M.gguf`	24.7 GB
`Ornith-1.5-35B-A3B-BigBang-MTP-Q6_K.gguf`	28.5 GB	recommended if you have the VRAM
`Ornith-1.5-35B-A3B-BigBang-MTP-Q8_0.gguf`	36.9 GB
`mtpdraft-Q8_0.gguf`	2.0 GB	MTP speculative draft — pass with `-md`
`Ornith-1.5-35B-A3B-BigBang-MTP-Q4_K_M-embedded.gguf`	~21.7 GB	Q4_K_M with the trained MTP head embedded (stock-style)

Usage

The MTP draft is optional. The main GGUFs contain no MTP tensors and work as normal models on their own (LM Studio, Ollama, plain llama.cpp):

llama-server -m Ornith-1.5-35B-A3B-BigBang-MTP-Q4_K_M.gguf -ngl 99 -c 4096 --jinja -fa on

Add the draft for ~+29% generation speed (outputs are unchanged; speculative decoding only affects speed):

llama-server -m Ornith-1.5-35B-A3B-BigBang-MTP-Q4_K_M.gguf \
  -md mtpdraft-Q8_0.gguf --spec-draft-n-max 4 --spec-draft-n-min 1 \
  -ngl 99 -c 4096 --jinja -fa on

mtpdraft-Q8_0.gguf also works as a -md draft for stock Ornith-1.5 GGUFs (same tower).

Reasoning is always on (<think>), so allow generous max_tokens. Suggested sampling: temp 0.1, top_k 50.

On a single RTX 3090 (Q4_K_M + Q8_0 draft) the draft lifts generation from 131 to 169 tok/s (+29%), with 55–75% acceptance (highest on code).

Merged and measured by EryriLabs (Dwain Barnes), 2026-08-20.

Downloads last month: 2,273

GGUF

Model size

2B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

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